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Published: 2014

Total Pages: 26

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We consider the joint source-channel coding (JSCC) problem where the real valued outputs of two correlated memoryless Gaussian sources are scalar quantized, bit assigned, and transmitted, without applying any error correcting code, over a multiple access channel (MAC) which consists of two orthogonal point-to-point time-correlated Rayleigh fading sub-channels with soft-decision demodulation. At the receiver side, a joint sequence maximum a posteriori (MAP) detector is used to exploit the correlation between the two sources as well as the redundancy left in the quantizers' indices, the channel's soft-decision outputs, and noise memory. The MAC's sub-channels are modeled via non-binary Markov noise discrete channels recently shown to effectively represent point-to-point fading channels. Two scenarios are studied. In the first scenario, the sources are memoryless and generated according to a bivariate Gaussian distribution with a given correlation parameter. In the second scenario, the sources have memory captured by a changing correlation parameter governed by a two state first order Markov process. In each scenario, for the simple case of quantizing the sources with two levels, we establish a necessary and a sufficient condition under which the joint sequence MAP decoder can be reduced to a simple instantaneous symbol-by-symbol decoder. Then, using numerical results obtained by system simulation, the theorems are illustrated and it is also verified that JSCC can harness the correlation between sources, redundancies in the source symbols, and statistics of the channel noise to achieve improved signal-to-distortion ratio (SDR) performance. For example, when the memoryless sources are highly correlated and soft-decision quantization is used, JSCC can profit from high correlation in the channel noise process and provide significant SDR gains of up to 6.3 dB over a fully interleaved channel.